Head and neck cancers represent approximately 3% of invasive cancers (about 55,000 patients) diagnosed annually in the United States. Approximately two thirds of all patients present with locally advanced disease. The best chance for cure of head and neck cancer is typically aggressive treatment at initial presentation including complete lymph node dissection. Accurate identification of nodal metastases is a crucial step for treatment planning and evaluation of therapy response. Knowledge of the extent of nodal metastases prior to surgery can help avoid unnecessary removal of lymph nodes. However, current imaging methods are not adequate for reliable assessment of metastatic nodes in both diagnostic imaging and post-treatment evaluation. The overarching goal of this study is to develop PET/MR techniques for accurate, non-invasive detection of nodal metastases for treatment planning and assessment of residual tumor in metastatic nodes after treatment, such that unnecessary removal of nodes can be minimized. A state-of-the-art PET/MR scanner will be utilized to acquire PET and MRI data simultaneously within one session. Our central hypothesis is that a combined model of FDG-PET and DCE-MRI measures can detect metastatic nodes more accurately than FDG-PET or DCE-MRI alone. This hypothesis can be best tested when both PET and MRI scans are simultaneously conducted in one setting, as it can minimize any physiological difference in tumor microenvironment between separate PET and MRI scans, in addition to the benefit of fewer visits for the participating patients. We propose to conduct dynamic PET/MR scans with head and neck cancer patients (n=50) scheduled for full node dissection surgery so that pathological evaluation of dissected nodes can be used as the reference standard to evaluate the accuracy of PET/MR measures. During the surgery, the level of nodes will be recorded for level- to-level comparison between PET/MR and pathology evaluation. The first phase of the study (Aim 1) is to assess the extent of which a combination of FDG-PET and DCE- MRI measures can identify metastatic nodes more accurately than FDG-PET or DCE-MRI alone. Aim 2 is to determine the association between the kinetic parameters of FDG-PET and DCE-MRI and investigate the complimentary roles of FDG-PET and DCE-MRI parameters. Transport rate constant from the proposed joint analysis will be compared with the expression level of glucose transporters on the tumor cell membrane using immunohistochemistry staining. If successful, the research in this proposal will establish a more reliable and accurate method to non-invasively detect metastatic lymph nodes in head and neck cancer. This method can then also be extended to evaluating nodal metastasis for diagnosis and post-treatment evaluation in other types of cancer.